NeoMHCI Training and Evaluation Data

All training and evaluation data used in the NeoMHCI study.

All code is freely available at https://github.com/ZhuLab-Fudan/NeoMHCI.

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Train Data:

1. EL_train.zip:
Contains all eluted ligand data used for training the five-fold cross-validation model.
- EL2020_A.txt: Corresponds to the EL2020_A dataset mentioned in the article.
- EL2020_B.txt: Corresponds to the EL2020_B dataset mentioned in the article.
Combining the data from these two files constitutes the EL2020_C dataset mentioned in the article.

2. NE_train.zip
Contains all neoepitope data used during the fine-tuning process for the immunogenicity prediction task.
- NE2023.txt: Corresponds to the NE2023 dataset mentioned in the article.
- NE2023_list.json: A candidate pool constructed from all wild-type sequences in NE2023.
- IN2023: Corresponds to the IN2023 dataset mentioned in the article, used as a validation set during the fine-tuning process.

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Test Data:

3. EL_test.zip
Contains the test set for ligand presentation prediction, along with the prediction scores of NeoMHCI and other comparison methods.
- IM2020.csv: Corresponds to the IM2020 test set mentioned in the article.
- IS2020.csv: Corresponds to the IS2020 test set mentioned in the article.

4. NE_test.zip
Contains the neoepitope test set for immunogenicity prediction, along with the prediction scores of NeoMHCI and other comparison methods.
- BM2023.csv: Corresponds to the BM2023 test set mentioned in the article.
- COVID-19.txt: Corresponds to the COVID-19 test set mentioned in the article.
- PM2018_data.txt: Corresponds to the PM2018 test set mentioned in the article. It includes `mutation_id` for mutation number, `patient_id` for patient number, `epitope` indicating whether the mutation is immunogenic, `tpm` for the gene expression level of the mutation, `cell_line` for the renamed multi-allele combination of the patient, with the specific correspondence in PM2018_allelelist. `pepseq` represents the specific sequence of the mutation. Each mutation is represented by all 8-11mer slices containing the mutation site, with the highest prediction value among all slices representing the prediction score for that mutation.
- PM2018_records.csv: Records the prediction scores of each method for every mutation with TPM>0.
- PM2018_allelelist: Records the multi-allele combinations expressed by each patient in PM2018.
- RV2023_data.txt, RV2023_records.txt, RV2023_allelelist: Same as PM2018

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Common:

- allelelist: Records the specific MHC-I molecule combinations corresponding to the names of the multi-allele combinations (cell line) used in the MA data.
- MHC_pseudo.dat: Records the 34-mer pseudo sequences of MHC-I molecules.
- eval.py: Evaluation script used to compile various metrics from the records of each test set.

本NeoMHCI研究中使用的全部训练与评测数据如下。所有代码均可免费获取于https://github.com/ZhuLab-Fudan/NeoMHCI。

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### 训练数据集
1. EL_train.zip：包含用于训练五折交叉验证（five-fold cross-validation）模型的全部洗脱配体（eluted ligand）数据。
- EL2020_A.txt：对应本文提及的EL2020_A数据集。
- EL2020_B.txt：对应本文提及的EL2020_B数据集。
将上述两个文件的数据合并，即可得到本文提及的EL2020_C数据集。

2. NE_train.zip：包含免疫原性预测（immunogenicity prediction）任务微调阶段使用的全部新表位（neoepitope）数据。
- NE2023.txt：对应本文提及的NE2023数据集。
- NE2023_list.json：从NE2023的全部野生型序列构建的候选池。
- IN2023：对应本文提及的IN2023数据集，在微调阶段用作验证集。

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### 测试数据集
3. EL_test.zip：包含配体呈递预测任务的测试集，以及NeoMHCI与其他对比方法的预测评分。
- IM2020.csv：对应本文提及的IM2020测试集。
- IS2020.csv：对应本文提及的IS2020测试集。

4. NE_test.zip：包含免疫原性预测任务的新表位测试集，以及NeoMHCI与其他对比方法的预测评分。
- BM2023.csv：对应本文提及的BM2023测试集。
- COVID-19.txt：对应本文提及的COVID-19测试集。
- PM2018_data.txt：对应本文提及的PM2018测试集。该文件包含以下字段：`mutation_id`（突变编号）、`patient_id`（患者编号）、`epitope`（用于标注该突变是否具有免疫原性）、`tpm`（突变所在基因的转录本表达水平）、`cell_line`（患者重命名后的多等位基因组合，即细胞系），其具体对应关系详见PM2018_allelelist；`pepseq`代表该突变对应的具体肽序列。每条突变由包含该突变位点的全部8~11肽剪切片段表示，所有片段中的最高预测值即为该突变的预测评分。
- PM2018_records.csv：记录了所有TPM>0的突变的各方法预测评分。
- PM2018_allelelist：记录了PM2018数据集中每位患者表达的多等位基因组合。
- RV2023_data.txt、RV2023_records.txt、RV2023_allelelist：格式与用途均与PM2018对应文件一致。

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### 通用文件
- allelelist：记录了MA数据中使用的多等位基因组合（即cell_line，细胞系）名称对应的具体MHC-I（主要组织相容性复合体I类）分子组合。
- MHC_pseudo.dat：记录了MHC-I分子的34肽段伪序列。
- eval.py：用于从各测试集的记录文件中计算各类评价指标的评测脚本。